In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import missingno as msno
In [2]:
df = pd.read_csv('EcomShipping.csv')
df.head(5).T
Out[2]:
0 1 2 3 4
ID 1 2 3 4 5
Warehouse_block D F A B C
Mode_of_Shipment Flight Flight Flight Flight Flight
Customer_care_calls 4 4 2 3 2
Customer_rating 2 5 2 3 2
Cost_of_the_Product 177 216 183 176 184
Prior_purchases 3 2 4 4 3
Product_importance low low low medium medium
Gender F M M M F
Discount_offered 44 59 48 10 46
Weight_in_gms 1233 3088 3374 1177 2484
Reached.on.Time_Y.N 1 1 1 1 1
In [3]:
df['Reached.on.Time_Y.N'] = df['Reached.on.Time_Y.N'].astype(str)
In [4]:
# Categoric data
df.describe(include='O')
Out[4]:
Warehouse_block Mode_of_Shipment Product_importance Gender Reached.on.Time_Y.N
count 10999 10999 10999 10999 10999
unique 5 3 3 2 2
top F Ship low F 1
freq 3666 7462 5297 5545 6563
In [5]:
# print out the categoric columns and its category 
for c in df.select_dtypes(exclude='number').columns.tolist():
    print(c, sorted(df[c].unique()))

Warehouse_block ['A', 'B', 'C', 'D', 'F']
Mode_of_Shipment ['Flight', 'Road', 'Ship']
Product_importance ['high', 'low', 'medium']
Gender ['F', 'M']
Reached.on.Time_Y.N ['0', '1']
In [6]:
# Numeric data
df.describe()
Out[6]:
ID Customer_care_calls Customer_rating Cost_of_the_Product Prior_purchases Discount_offered Weight_in_gms
count 10999.00000 10999.000000 10999.000000 10999.000000 10999.000000 10999.000000 10999.000000
mean 5500.00000 4.054459 2.990545 210.196836 3.567597 13.373216 3634.016729
std 3175.28214 1.141490 1.413603 48.063272 1.522860 16.205527 1635.377251
min 1.00000 2.000000 1.000000 96.000000 2.000000 1.000000 1001.000000
25% 2750.50000 3.000000 2.000000 169.000000 3.000000 4.000000 1839.500000
50% 5500.00000 4.000000 3.000000 214.000000 3.000000 7.000000 4149.000000
75% 8249.50000 5.000000 4.000000 251.000000 4.000000 10.000000 5050.000000
max 10999.00000 7.000000 5.000000 310.000000 10.000000 65.000000 7846.000000

Data cleaning (check)¶

In [7]:
msno.bar(df, color = 'orange')
plt.title('Checking for Missing Values\n', fontsize = 40)
plt.show()
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heatmap of the data for checking the correlation between the features and target column.¶

In [8]:
plt.figure(figsize = (18, 7))
sns.heatmap(df.select_dtypes(include='number').corr(), annot=True, fmt='0.2f', 
            annot_kws={'size': 15}, linewidth=2, linecolor='orange')
plt.show()
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Checking value counts of columns¶

In [9]:
# create columns list to check
cols = ['Warehouse_block', 'Mode_of_Shipment', 'Customer_care_calls', 'Customer_rating',
        'Prior_purchases', 'Product_importance', 'Gender', 'Reached.on.Time_Y.N']

plt.figure(figsize = (16, 20))

# plotting the countplot of each categorical column.
for i, col in enumerate(cols):
    if i <= 8:
        ax = plt.subplot(4, 2, i+1)
        sns.countplot(x = col, data = df, ax = ax, palette='rocket')
        plt.title(f"\n{col} Value Counts\n", fontsize = 20)

plt.tight_layout()
plt.show()

C:\Users\USER\AppData\Local\Temp\ipykernel_9760\1267449992.py:11: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x = col, data = df, ax = ax, palette='rocket')
C:\Users\USER\AppData\Local\Temp\ipykernel_9760\1267449992.py:11: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x = col, data = df, ax = ax, palette='rocket')
C:\Users\USER\AppData\Local\Temp\ipykernel_9760\1267449992.py:11: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x = col, data = df, ax = ax, palette='rocket')
C:\Users\USER\AppData\Local\Temp\ipykernel_9760\1267449992.py:11: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x = col, data = df, ax = ax, palette='rocket')
C:\Users\USER\AppData\Local\Temp\ipykernel_9760\1267449992.py:11: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x = col, data = df, ax = ax, palette='rocket')
C:\Users\USER\AppData\Local\Temp\ipykernel_9760\1267449992.py:11: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x = col, data = df, ax = ax, palette='rocket')
C:\Users\USER\AppData\Local\Temp\ipykernel_9760\1267449992.py:11: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x = col, data = df, ax = ax, palette='rocket')
C:\Users\USER\AppData\Local\Temp\ipykernel_9760\1267449992.py:11: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x = col, data = df, ax = ax, palette='rocket')
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Exploring relation of categorical columns¶

In [10]:
object_columns = df.select_dtypes(include = ['object'])
object_columns.sample(5)
Out[10]:
Warehouse_block Mode_of_Shipment Product_importance Gender Reached.on.Time_Y.N
4106 A Flight low F 1
7974 D Ship medium M 0
1089 B Road medium F 1
10896 D Ship low F 0
1487 F Road low F 1

Warehouse column and categories proportion¶

In [11]:
warehouse = object_columns['Warehouse_block'].value_counts().reset_index()
warehouse.columns = ['warehouse', 'value_counts']
fig = px.pie(warehouse, names = 'warehouse', values = 'value_counts', 
             color_discrete_sequence = px.colors.sequential.matter_r, width = 650, height = 400,
             hole = 0.5)
fig.update_traces(textinfo = 'percent+label')

Reach on time count in warehouse block¶

In [12]:
# 1 : NOT on time and 0: on time
plt.figure(figsize = (17, 6))
sns.countplot(data = df, x='Warehouse_block', hue = 'Reached.on.Time_Y.N',  palette='rocket')
plt.show()
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Gender¶

In [13]:
gender = object_columns['Gender'].value_counts().reset_index()
gender.columns = ['Gender', 'value_counts']
fig = px.pie(gender, names = 'Gender', values = 'value_counts',  
             width = 650, height = 400, hole = 0.4, 
             color_discrete_sequence=px.colors.sequential.RdBu)
fig.update_traces(textinfo = 'percent+label')
In [14]:
# 1 : NOT on time and 0: on time
plt.figure(figsize = (17, 6))
sns.countplot(x='Gender', hue = 'Reached.on.Time_Y.N', data = df, palette='rocket')
plt.show()
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Shipment method¶

In [15]:
mode = object_columns['Mode_of_Shipment'].value_counts().reset_index()
mode.columns = ['Mode_of_Shipment', 'value_counts']
fig = px.pie(mode, names = 'Mode_of_Shipment', values = 'value_counts', 
             color_discrete_sequence = px.colors.sequential.RdBu, 
             width = 650, height = 400, hole = 0.5)
fig.update_traces(textinfo = 'percent+label')
fig.show()

Reach on time vs Shipping method¶

In [16]:
plt.figure(figsize = (17, 6))
sns.countplot(x='Mode_of_Shipment', hue = 'Reached.on.Time_Y.N', 
              data = df, palette='rocket')
plt.show()
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Product importance¶

In [17]:
product_imp = object_columns['Product_importance'].value_counts().reset_index()
product_imp.columns = ['Product_importance', 'value_counts']
fig = px.pie(product_imp, names = 'Product_importance', values = 'value_counts',
             color_discrete_sequence = px.colors.sequential.RdBu, 
             width = 650, height = 400, hole = 0.5)
fig.update_traces(textinfo = 'percent+label')

Product importance Shipment on time¶

In [18]:
# 1 : NOT on time and 0: on time
plt.figure(figsize = (17, 6))
sns.countplot(x='Product_importance', hue = 'Reached.on.Time_Y.N', 
              data = df, palette='rocket')
plt.show()
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Relation of continuous columns with on time or not¶

In [19]:
integer_columns = df.select_dtypes(include = ['int64'])
integer_columns.head()
Out[19]:
ID Customer_care_calls Customer_rating Cost_of_the_Product Prior_purchases Discount_offered Weight_in_gms
0 1 4 2 177 3 44 1233
1 2 4 5 216 2 59 3088
2 3 2 2 183 4 48 3374
3 4 3 3 176 4 10 1177
4 5 2 2 184 3 46 2484

Customer care calls¶

In [20]:
customer_care = integer_columns['Customer_care_calls'].value_counts().reset_index()
customer_care.columns = ['Customer_care_calls', 'value_counts']
fig = px.pie(customer_care, names = 'Customer_care_calls', 
             values = 'value_counts', width = 650, height = 400,
             color_discrete_sequence = px.colors.sequential.RdBu, hole = 0.4)
fig.update_traces(textinfo = 'percent+label')

Customer call vs Shipment on time¶

In [21]:
# 1 : NOT on time and 0: on time
plt.figure(figsize = (17, 6))
sns.countplot(data = df, x='Customer_care_calls',   
              palette='rocket', hue = 'Reached.on.Time_Y.N')
plt.show()
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Customers' rating¶

In [22]:
customer_ratings = integer_columns['Customer_rating'].value_counts().reset_index()
customer_ratings.columns = ['Customer_rating', 'value_counts']
fig = px.pie(customer_ratings, names = 'Customer_rating', values = 'value_counts',
             color_discrete_sequence = px.colors.sequential.RdBu, 
             width = 650, height = 400, hole = 0.4)
fig.update_traces(textinfo = 'percent+label')

Customers rating vs Shipment on time¶

In [23]:
# 1 : NOT on time and 0: on time
plt.figure(figsize = (17, 6))
sns.countplot(x='Customer_rating', hue = 'Reached.on.Time_Y.N', 
              data = df, palette='rocket')
plt.show()
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Prior purchase¶

In [24]:
prior_purchases = integer_columns['Prior_purchases'].value_counts().reset_index()
prior_purchases.columns = ['Prior_purchases', 'value_counts']
fig = px.pie(prior_purchases, names = 'Prior_purchases', values = 'value_counts',
             color_discrete_sequence = px.colors.sequential.RdBu, 
             width = 650, height = 400, hole = 0.4)
fig.update_traces(textinfo = 'percent+label')

Prior purchase vs shipment on time¶

In [25]:
# 1 : NOT on time and 0: on time
plt.figure(figsize = (17, 6))
sns.countplot(x='Prior_purchases', hue = 'Reached.on.Time_Y.N', data = df, palette='rocket')
plt.show()
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Reach on time vs not on time¶

In [26]:
# 1 : NOT on time and 0: on time
reached_on_time_y_n = df['Reached.on.Time_Y.N'].value_counts().reset_index()
reached_on_time_y_n.columns = ['Reached.on.Time_Y.N', 'value_counts']
fig = px.pie(reached_on_time_y_n, names = 'Reached.on.Time_Y.N', values = 'value_counts',
             color_discrete_sequence = px.colors.sequential.RdBu, 
             width = 650, height = 400, hole = 0.4)
fig.update_traces(textinfo = 'percent+label')

Cost of the product¶

In [27]:
plt.figure(figsize = (15, 7))
ax = sns.histplot(df['Cost_of_the_Product'], bins = 100, color = 'orange', kde=True)

plt.show()
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relation between cost of the product and shipment on time¶

In [28]:
# 1 : NOT on time and 0: on time
px.box(data_frame = df, x = 'Reached.on.Time_Y.N', y = 'Cost_of_the_Product', 
       color = 'Reached.on.Time_Y.N' )
In [ ]:

=== Part 2 ===¶

Discount offered distribution¶

In [29]:
plt.figure(figsize = (15, 7))
ax = sns.histplot(df['Discount_offered'], color = 'b', kde=True)
plt.show()
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Relation between discount offered vs Shipment on time¶

In [30]:
# 1 : NOT on time and 0: on time
px.box(data_frame = df, x = 'Reached.on.Time_Y.N', y = 'Discount_offered', 
       color = 'Reached.on.Time_Y.N')

Weight in grams¶

In [31]:
plt.figure(figsize = (15, 7))
ax = sns.histplot(df['Weight_in_gms'], bins = 100, color = 'purple', kde=True)
plt.show()
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Relation between weights in grams and Shipment on time¶

In [32]:
# 1 : NOT on time and 0: on time
px.box(data_frame = df, x = 'Reached.on.Time_Y.N', y = 'Weight_in_gms', 
       color = 'Reached.on.Time_Y.N', )

Which warehouse contains most weights?¶

In [33]:
ware_block_weight = df.groupby(['Warehouse_block'])['Weight_in_gms'].sum().reset_index()
ware_block_weight
Out[33]:
Warehouse_block Weight_in_gms
0 A 6627118
1 B 6664240
2 C 6674560
3 D 6655305
4 F 13349327
In [34]:
px.histogram(data_frame = df, x = 'Weight_in_gms', nbins = 100, 
             color = 'Warehouse_block', marginal = 'box')

Mode of shipment vs Weight in grams¶

In [35]:
shipment_mode_weight = df.groupby(['Mode_of_Shipment'])['Weight_in_gms'].sum().reset_index()
shipment_mode_weight
Out[35]:
Mode_of_Shipment Weight_in_gms
0 Flight 6449405
1 Road 6423209
2 Ship 27097936
In [36]:
px.histogram(data_frame = df, x = 'Weight_in_gms', nbins = 100, 
             color = 'Mode_of_Shipment', marginal = 'box')

Warehouse vs Cost of product¶

In [37]:
warehouse_weight = df.groupby(['Warehouse_block'])['Cost_of_the_Product'].sum().reset_index()
warehouse_weight
Out[37]:
Warehouse_block Cost_of_the_Product
0 A 382671
1 B 388888
2 C 387114
3 D 386805
4 F 766477
In [38]:
px.histogram(data_frame = df, x = 'Cost_of_the_Product', nbins = 100, 
             color = 'Warehouse_block', marginal = 'box')

Cost of product vs Shipment mode¶

In [39]:
mode_shipment_cost = df.groupby(['Mode_of_Shipment'])['Cost_of_the_Product'].sum().reset_index()
mode_shipment_cost
Out[39]:
Mode_of_Shipment Cost_of_the_Product
0 Flight 371938
1 Road 370437
2 Ship 1569580
In [40]:
px.histogram(data_frame = df, x = 'Cost_of_the_Product', nbins = 100, 
             color = 'Mode_of_Shipment',  marginal = 'box')

Customer call effect on ratings?¶

In [41]:
plt.figure(figsize = (18, 9))
sns.lineplot(x = 'Customer_care_calls', y = 'Customer_rating', hue = 'Gender', data = df,
             palette = 'rocket', errorbar=('ci', 0))
plt.title('Relation between Customer Care Calls and Customer Rating of Males and Females\n',
          fontsize = 15)
plt.show()
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Relation between customer care calls, customer ratings and shipmetn on time¶

In [42]:
plt.figure(figsize = (18, 7))
sns.barplot(x = 'Customer_care_calls', y = 'Customer_rating', 
            hue = 'Reached.on.Time_Y.N', data = df, palette = 'rocket')
plt.ylim(0, 5)
plt.show()
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Relation between Customer calls, Prior purchase and Product importance¶

In [43]:
plt.figure(figsize = (18, 8))
sns.barplot(x = 'Prior_purchases', y = 'Customer_care_calls', data = df, 
            hue = 'Product_importance', palette = 'rocket')
plt.show()
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Product importance vs Customer call on Shipment on time¶

In [44]:
plt.figure(figsize = (18, 10))
sns.barplot(x='Product_importance', y = 'Customer_care_calls', 
            hue = 'Reached.on.Time_Y.N', data = df, palette = 'rocket')
plt.show()
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Product importance and Discount offered¶

In [45]:
px.box(data_frame = df, x = 'Product_importance', y ='Discount_offered', 
       color = 'Product_importance')

Cost of product vs Product importance on Shipment on time¶

In [46]:
# 1 : NOT on time and 0: on time
px.box(data_frame = df, x = 'Product_importance', y ='Cost_of_the_Product', 
       color = 'Reached.on.Time_Y.N')

Relation Prior_purchases and Discount Offered¶

In [47]:
px.box(x = 'Prior_purchases', y = 'Discount_offered', data_frame = df, 
       color = 'Prior_purchases')

Relation Prior_purchases and Discount Offered and Shipment on time¶

In [48]:
# 1 : NOT on time and 0: on time
px.box(x = 'Prior_purchases', y = 'Discount_offered', data_frame = df, 
       color = 'Reached.on.Time_Y.N')

Customer care calls and weight in grams¶

In [49]:
px.box(x = 'Customer_care_calls', y = 'Weight_in_gms', data_frame = df, 
       color = 'Customer_care_calls')
In [50]:
## Customer care calls and weight in grams on shipment on time
In [51]:
# 1 : NOT on time and 0: on time
px.box(x = 'Customer_care_calls', y = 'Weight_in_gms', data_frame = df, 
       color = 'Reached.on.Time_Y.N')

Relation of Prior purchase and Weight¶

In [52]:
px.box(x = 'Prior_purchases', y = 'Weight_in_gms', data_frame = df, 
       color = 'Prior_purchases')

Relatio of Prior purchases, Weights in gram and Shipment on time¶

In [53]:
# 1 : NOT on time and 0: on time
px.box(x = 'Prior_purchases', y = 'Weight_in_gms', data_frame = df, 
       color = 'Reached.on.Time_Y.N')

Relation of prior purchases and cost of the products¶

In [54]:
px.box(x = 'Prior_purchases', y = 'Cost_of_the_Product', data_frame = df, 
       color = 'Prior_purchases')

Prior purchases and Products cost on Shipment on time¶

In [55]:
# 1 : NOT on time and 0: on time
px.box(x = 'Prior_purchases', y = 'Cost_of_the_Product', data_frame = df, 
       color = 'Reached.on.Time_Y.N')

Relation of cost of the products and customer care calls¶

In [56]:
px.box(x = 'Customer_care_calls', y = 'Cost_of_the_Product', data_frame = df, 
       color = 'Customer_care_calls')

Product cost and Customer call on Shipment on time¶

In [57]:
# 1 : NOT on time and 0: on time
px.box(x = 'Customer_care_calls', y = 'Cost_of_the_Product', data_frame = df, 
       color = 'Reached.on.Time_Y.N')

Relation between Cost of the product and Discount offered and Shipment on time¶

In [58]:
# 1 : NOT on time and 0: on time
plt.figure(figsize = (15, 7))
sns.scatterplot(x='Discount_offered', y='Cost_of_the_Product', 
                data=df, hue='Reached.on.Time_Y.N')
plt.show()
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In [ ]: